摘要
及时获取田间作物的土壤水分,对指导精准灌溉有重要意义。以抽穗期冬小麦为研究对象,采用低空无人机搭载六波段多光谱相机获取其冠层光谱反射率,并与参考点光谱反射率求差得差值反射率(DR),不同深度的土壤含水率(0~10、0~20、0~30、0~40、0~60 cm)与参考点土壤含水率同样求差得差值土壤含水率(DSM),对DR与DSM进行相关性分析,分别建立2者的一元线性模型和多元线性回归模型并验证。结果表明:一元模型中,40 cm深度的优于60 cm,20 cm的预测效果不佳;多元模型中,宽行距中40 cm深度的最优,建模R2和验证R2均达到了0.9以上,预测均方根误差仅为0.016。该研究可大面积快速获取田间土壤水分,为精准灌溉提供一定的理论依据。
The timely acquisition of soil moisture is of great significance to guide precision irrigation. Winter wheat at heading stage is studied in this paper. The spectral information of winter wheat canopy is collected by an unmanned aerial vehicle( UAV) equipped with a multi-spectral camera of six bands aiming at getting the difference( DR) between the spectral reflectance of reference point and that of each plot.Then,the difference( DSM) between the soil moisture content of reference point and that of each plot is calculated in the similar way.Through analyzing the relationship between DR and DSM,the single linear models and multivariate linear regression models are established and validated. The results show that the model of 40 cm is better than that of 60 cm in the single models and the model of 40 cm in wide spacing is the best with R2( determination coefficient) up to 0.9 and the prediction of RMSE( Root Mean Square Error) only 0.016 in the multivariate models. The study can quickly obtain soil moisture content in field to provide some theoretical basis for precision irrigation.
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